ArticleSurgical endoscopy2021
Applying the electronic nose for pre-operative SARS-CoV-2 screening.
Article in Surgical endoscopy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 3 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
35 citing papers in PubMed, 3 syntheses or guidelines pooled it, 81 citations in OpenAlex.
- A comprehensive meta-analysis and systematic review of breath analysis in detection of COVID-19 through Volatile organic compounds.Diagnostic microbiology and infectious disease · 2024Pooled it
- The potential of volatile organic compounds-based breath analysis for COVID-19 screening: a systematic review & meta-analysis.Diagnostic microbiology and infectious disease · 2022Pooled it
- Accuracy of the Electronic Nose Breath Tests in Clinical Application: A Systematic Review and Meta-Analysis.Biosensors · 2021Pooled it
- Untargeted Sweat and Sebum Volatilomics by HS-SPME-GC/ToF-MS for the Identification of SARS-CoV-2-Associated Biomarkers.Metabolites · 2026Article
- Examination of unique volatile organic compound signatures in nasopharyngeal test swab viral transport media using an electronic nose.PloS one · 2025Article
- COVID-19: An overview on possible transmission ways, sampling matrices and diagnosis.BioImpacts : BI · 2024Review
- RApid Throughput Screening for Asymptomatic COVID-19 Infection With an Electrocardiogram: A Prospective Observational Study.Mayo Clinic proceedings. Digital health · 2023Article
- Effects of machine learning-based clinical decision support systems on decision-making, care delivery, and patient outcomes: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Rapid point-of-care detection of SARS-CoV-2 infection in exhaled breath using ion mobility spectrometry: a pilot study.European journal of medical research · 2023Article
- Diagnostic performance of eNose technology in COVID-19 patients after hospitalization.BMC pulmonary medicine · 2023Article
- Potential for Early Noninvasive COVID-19 Detection Using Electronic-Nose Technologies and Disease-Specific VOC Metabolic Biomarkers.Sensors (Basel, Switzerland) · 2023Review
- Portable Breath-Based Volatile Organic Compound Monitoring for the Detection of COVID-19 During the Circulation of the SARS-CoV-2 Delta Variant and the Transition to the SARS-CoV-2 Omicron Variant.JAMA network open · 2023Article
- Breath Analysis of COVID-19 Patients in a Tertiary UK Hospital by Optical Spectrometry: The E-Nose CoVal Study.Biosensors · 2023Article
- Application of Machine Learning Methods for an Analysis of E-Nose Multidimensional Signals in Wastewater Treatment.Sensors (Basel, Switzerland) · 2023Article
- Noninvasive Detection of Bacterial Infection in Children Using Piezoelectric E-Nose.Sensors (Basel, Switzerland) · 2022Article
- Breath Biomarkers of Influenza Infection.Open forum infectious diseases · 2022Article
- Clinical studies of detecting COVID-19 from exhaled breath with electronic nose.Scientific reports · 2022Article
- Sustainable solutions for indoor pollution abatement during COVID phase: A critical study on current technologies & challenges.Journal of hazardous materials advances · 2022Article
- Review
- Hybrid learning method based on feature clustering and scoring for enhanced COVID-19 breath analysis by an electronic nose.Artificial intelligence in medicine · 2022Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundInfection with SARS-CoV-2 causes corona virus disease (COVID-19). The most standard diagnostic method is reverse transcription-polymerase chain reaction (RT-PCR) on a nasopharyngeal and/or an oropharyngeal swab. The high occurrence of false-negative results due to the non-presence of SARS-CoV-2 in the oropharyngeal environment renders this sampling method not ideal. Therefore, a new sampling device is desirable. This proof-of-principle study investigated the possibility to train machine-learning classifiers with an electronic nose (Aeonose) to differentiate between COVID-19-positive and negative persons based on volatile organic compounds (VOCs) analysis.
methodsBetween April and June 2020, participants were invited for breath analysis when a swab for RT-PCR was collected. If the RT-PCR resulted negative, the presence of SARS-CoV-2-specific antibodies was checked to confirm the negative result. All participants breathed through the Aeonose for five minutes. This device contains metal-oxide sensors that change in conductivity upon reaction with VOCs in exhaled breath. These conductivity changes are input data for machine learning and used for pattern recognition. The result is a value between - 1 and + 1, indicating the infection probability.
results219 participants were included, 57 of which COVID-19 positive. A sensitivity of 0.86 and a negative predictive value (NPV) of 0.92 were found. Adding clinical variables to machine-learning classifier via multivariate logistic regression analysis, the NPV improved to 0.96.
conclusionsThe Aeonose can distinguish COVID-19 positive from negative participants based on VOC patterns in exhaled breath with a high NPV. The Aeonose might be a promising, non-invasive, and low-cost triage tool for excluding SARS-CoV-2 infection in patients elected for surgery.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.